This project begins from the simple premise that the task of
endogenizing the NK search landscape can be done by representing the
search environment as a binary bipartite network of M
actors affiliating with N components. This bipartite
network can be then be analyzed according to the Stochastic
Actor-Oriented Model (SAOM) (Snijders,
1996).
Thus, the Stochastic Actor-Oriented MNK model, abbrivated
SaoMNK, is designed for running strategic search
simulation, testing, and experimentation by leveraging the
RSiena package, an R implementation of
Simulation Investigation for Empirical Network Analysis (SIENA).
This tutorial offers a basic introduction to SaoMNK.
############### Load R6 Class DEPENDENCIES ############################
## Biparite Environment Search Simulation Class
SaomNkRSienaBiEnv <- source(file.path(dir_r, 'SAOM_NK_R6_model.R'))$value
# ## RSiena search Class
# SaomNkRSienaBiEnv_search_rsiena <- source(file.path(dir_proj, 'SAOM_NK_R6_search_rsiena_model.R'))$value
## default settings: Users do not change; TODO: implment within restricted class attributes
DV_NAME <- 'self$bipartite_rsienaDV'
#
steps_per_actor <- 25
#
environ_params <- list(
M = 12, ## Actors
N = 9, ## Components
BI_PROB = 0, ## Environmental Density (DGP hyperparameter)
component_matrix_start = 'rand', ##**TODO** Implement: 'rand','modular','semi-modular',...
rand_seed = 1234,
plot_init = F,
name = '_test_tutorial_nb_'
)
#
env1 <- SaomNkRSienaBiEnv$new(environ_params)
##
## TEST FROM CALLED CLASS INIT *BEFORE* BASE INIT
##
## CALLED _BASE_ INIT
##
## TEST FROM CALLED CLASS INIT *AFTER* BASE INIT
print(c(env1$M, env1$N))
## [1] 12 9
library(Matrix) # For block diagonal matrices
#
create_block_diag <- function(N, B) {
# Determine the approximate block size
block_sizes <- rep(N %/% B, B) # Equal-sized blocks
block_sizes[1:(N %% B)] <- if (N %% B == 0) {
block_sizes[1:(N %% B)] # Adjust for remainder
} else {
block_sizes[1:(N %% B)] + 1
}
# Create individual binary blocks
blocks <- lapply(block_sizes, function(s) matrix(1, nrow = s, ncol = s))
# Combine blocks into a block diagonal matrix
block_diag_matrix <- as.matrix(bdiag(blocks)) # Convert sparse to standard matrix
# Ensure exact NxN dimensions
block_diag_matrix <- block_diag_matrix[1:N, 1:N] # Trim to N x N
# return
return(block_diag_matrix)
}
# Example usage:
N <- 6 # Number of rows/columns
B <- 2 # Number of blocks
block_matrix <- create_block_diag(8, 2)
# Print the matrix
print(block_matrix)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 1 1 1 0 0 0 0
## [2,] 1 1 1 1 0 0 0 0
## [3,] 1 1 1 1 0 0 0 0
## [4,] 1 1 1 1 0 0 0 0
## [5,] 0 0 0 0 1 1 1 1
## [6,] 0 0 0 0 1 1 1 1
## [7,] 0 0 0 0 1 1 1 1
## [8,] 0 0 0 0 1 1 1 1
SAOM Objective Function serves as the stochastic actor’s utility function for strategic search.
#
strategies <- list(
egoX = c(-1, 0, 1),
inPopX = c( 1, 0, -1)
)
## 2.b. Component Payoffs vector
set.seed(12345)
component_payoffs <- runif(environ_params$N, min = 0, max = 1)
## 2. Strategies sets the objective function as a linear combination of network stats across DVs
#
actor_strats_list <- lapply(strategies, function(strat) rep(strat, environ_params$M/length(strat)) )
component_int_mat <- create_block_diag(environ_params$N, round(environ_params$N/3))
# dyad_cov_XWX <- ( outer(component_payoffs, component_payoffs, '*') *
# create_block_diag(environ_params$N, round(environ_params$N/3))
# )
dyad_cov_X <- matrix(runif(n = environ_params$N*environ_params$M, min = -.5, max= 1), nrow=environ_params$M)
# dyad_cov_X <- matrix(runif(environ_params$N*environ_params$M) - runif(environ_params$N*environ_params$M),
# nrow=environ_params$M)
# dyad_cov_XWX_X <- t( dyad_cov_XWX %*% t(dyad_cov_X) )
#
structure_model <- list(
dv_bipartite = list(
name = 'self$bipartite_rsienaDV',
effects = list( ##**STRUCTURAL EFFECTS -- dyadic/network endogeneity sources**
list(effect='density', parameter= 0, dv_name=DV_NAME, fix=T ), ##interaction1 = NULL
list(effect='inPop', parameter= 0, dv_name=DV_NAME, fix=T ), #interaction1 = NUL
list(effect='outAct', parameter= 0, dv_name=DV_NAME, fix=T )
),
## COVARIATE EFFECTS
coCovars = list(
##** COMPONENTS : MONADIC CONSTANT COVARIATE EFFECTS **##
# list(effect='altX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
# list(effect='outActX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
##** STRATEGIES : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='egoX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar', x = actor_strats_list[[1]]
), #interaction1 = NULL
list(effect='inPopX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar', x = actor_strats_list[[2]]
) #,
# list(effect='totInDist2', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$strat_3_coCovar', x = (actor_strats_list[[1]] - actor_strats_list[[2]] )
# )
),
##**MONADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC STRATEGY PROGRAMS**
varCovars = list(),
##**DYADIC CONSTANT COVARIATE EFFECTS -- EXOGENOUS INTERACTION MATRIX**
coDyadCovars = list(
list(effect='XWX', parameter= 1, dv_name=DV_NAME, fix=T,
interaction1='self$component_1_coDyadCovar',
x = component_int_mat ## component-[actor]-component dyads
) ,
list(effect='X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$component_2_coDyadCovar',
x = dyad_cov_X ## deltas = changes of payoff contributions from each actor-component
)
),
##**DYADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC INTERACTION MATRIX**
varDyadCovars = list(),
interactions = list(
list(effect='egoX|XWX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar',
interaction2='self$component_1_coDyadCovar'
),
list(effect='inPopX|X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar',
interaction2='self$component_2_coDyadCovar'
)
)
)
)
env1$preview_effects(structure_model, filter=FALSE)
## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html .
# ## Uncomment for HTML output with filterable data table
env1$preview_effects(structure_model, filter=TRUE)
## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html .
## TODO: PICK UP WITH coDydCovar Interation Matrix
## Run Rsiena search using variable parameters in theta_matrix
env1$search_rsiena(
structure_model,
iterations = env1$M * steps_per_actor,
digits = 4,
run_seed = 12345
)
## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
##
##
## self$rsiena_data :
##
## Dependent variables: self$bipartite_rsienaDV
## Number of observations: 2
##
## Nodesets ACTORS COMPONENTS
## Number of nodes 12 9
##
## Dependent variable self$bipartite_rsienaDV
## Type bipartite
## Observations 2
## First nodeset ACTORS
## Second nodeset COMPONENTS
## Densities NA NA
##
## Constant covariates: self$strat_1_coCovar, self$strat_2_coCovar
## Constant dyadic covariates: self$component_1_coDyadCovar, self$component_2_coDyadCovar
##
## structural effects i=1, j=1
## $effect
## [1] "density"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE -1.60944 0
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=2
## $effect
## [1] "inPop"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=3
## $effect
## [1] "outAct"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=1
## $effect
## [1] "egoX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $x
## [1] -1 0 1 -1 0 1 -1 0 1 -1 0 1
##
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=2
## $effect
## [1] "inPopX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $x
## [1] 1 0 -1 1 0 -1 1 0 -1 1 0 -1
##
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 1
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 0
##
## coDyadCovars i=1, j=1
## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 0
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 1
##
## coDyadCovars i=1, j=2
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## There is no effect with short name X,
## and with interaction1 = <>, interaction2 = <>, and type = <eval>,
## for dependent variable self$bipartite_rsienaDV .
## See effectsDocumentation() for this effects object.
## effectName include fix test initialValue parm
## 1 self$component_2_coDyadCovar TRUE TRUE FALSE 0 0
##
## interactions i=1, j=1
## $effect
## [1] "egoX|XWX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $interaction2
## [1] "self$component_1_coDyadCovar"
##
## $effects
## [1] "egoX" "XWX"
##
## effectName
## 1 self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 88 80
##
## interactions i=1, j=2
## $effect
## [1] "inPopX|X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $interaction2
## [1] "self$component_2_coDyadCovar"
##
## $effects
## [1] "inPopX" "X"
##
## effectName
## 1 ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 169 85
## [1] "density"
## [1] "inPop"
## [1] "outAct"
## [1] "XWX"
## [1] "X"
## [1] "egoX"
## [1] "inPopX"
## [1] "egoX|XWX"
## [1] "inPopX|X"
##
##
## theta_matrix :
##
## density inPop outAct XWX X egoX inPopX egoX|XWX inPopX|X
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## If you use this algorithm object, siena07 will create/use an output file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_test_tutorial_nb__173926268866.txt .
##
## Start phase 0
## theta: 0 0 0 0 0 0 0 0 0
##
## Start phase 3
## Phase 3 Iteration 100 Progress 33%
## Phase 3 Iteration 200 Progress 67%
## Phase 3 Iteration 300 Progress 100%
## Parameter values used for simulations
##
## Mean Standard
## value Deviation
##
## Rate parameters:
## 0 Rate parameter NA ( NA )
##
## Other parameters:
## 1. eval outdegree (density) 0 ( NA )
## 2. eval indegree - popularity 0 ( NA )
## 3. eval outdegree - activity 0 ( NA )
## 4. eval XW=>X closure of self$component_1_coDyadCovar 1 ( NA )
## 5. eval self$component_2_coDyadCovar 0 ( NA )
## 6. eval self$strat_1_coCovar ego 0 ( NA )
## 7. eval ind. pop.^(1/0) weighted self$strat_2_coCovar 0 ( NA )
## 8. eval self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0 ( NA )
## 9. eval ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0 ( NA )
##
## Simulated means and standard deviations
## 1. Number of ties 0.867 0.341
## 2. Sum of squared indegrees 0.867 0.341
## 3. Sum of squared outdegrees 0.867 0.341
## 4. XW=>X closure of self$component_1_coDyadCovar 0.000 0.000
## 5. Sum of ties x self$component_2_coDyadCovar -0.037 0.418
## 6. Sum of outdegrees x self$strat_1_coCovar -0.063 0.758
## 7. indegree pop.^(1/0) weighted self$strat_2_coCovar 0.063 0.758
## 8. self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.000 0.000
## 9. ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.071 0.316
##
##
## Simulated statistics are in x$sf
## and simulated dependent variables in x$sims, where x is the created object.
##
## Total of 300 iteration steps.
##
## Covariance matrix of estimates (correlations below diagonal)
##
## 0 0 0 0 0 0 0 0 0
## NaN 0 0 0 0 0 0 0 0
## NaN NaN 0 0 0 0 0 0 0
## NaN NaN NaN 0 0 0 0 0 0
## NaN NaN NaN NaN 0 0 0 0 0
## NaN NaN NaN NaN NaN 0 0 0 0
## NaN NaN NaN NaN NaN NaN 0 0 0
## NaN NaN NaN NaN NaN NaN NaN 0 0
## NaN NaN NaN NaN NaN NaN NaN NaN 0
##
## Derivative matrix of expected statistics X by parameters:
##
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## -0.005 -0.005 -0.005 0.000 0.152 -0.018 0.018 0.000 0.008
## 0.007 0.007 0.007 0.000 -0.008 0.057 -0.057 0.000 0.003
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
##
## Covariance matrix of X (correlations below diagonal):
##
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 1.000 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 1.000 1.000 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## NaN NaN NaN 0.000 0.000 0.000 0.000 0.000 0.000
## -0.035 -0.035 -0.035 NaN 0.175 -0.074 0.074 0.000 0.008
## -0.033 -0.033 -0.033 NaN -0.233 0.575 -0.575 0.000 0.029
## 0.033 0.033 0.033 NaN 0.233 -1.000 0.575 0.000 -0.029
## NaN NaN NaN NaN NaN NaN NaN 0.000 0.000
## 0.089 0.089 0.089 NaN 0.059 0.119 -0.119 NaN 0.100
##
##
##
## Simulated Decision Chain Summary:
##
## dv_type dv_type_bin dv_varname id_from
## Length:300 Min. :0 Length:300 Min. : 1.000
## Class :character 1st Qu.:0 Class :character 1st Qu.: 4.000
## Mode :character Median :0 Mode :character Median : 7.000
## Mean :0 Mean : 6.757
## 3rd Qu.:0 3rd Qu.:10.000
## Max. :0 Max. :12.000
## id_to beh_difference reciprocal_rate LogOptionSetProb
## Min. : 1.0 Min. :0 Min. :0.08333 Min. :-2.485
## 1st Qu.: 3.0 1st Qu.:0 1st Qu.:0.08333 1st Qu.:-2.485
## Median : 6.0 Median :0 Median :0.08333 Median :-2.485
## Mean : 5.6 Mean :0 Mean :0.08333 Mean :-2.485
## 3rd Qu.: 8.0 3rd Qu.:0 3rd Qu.:0.08333 3rd Qu.:-2.485
## Max. :10.0 Max. :0 Max. :0.08333 Max. :-2.485
## LogChoiceProb diagonal stability tie_change
## Min. :-2.303 Length:300 Mode :logical Mode :logical
## 1st Qu.:-2.303 Class :character FALSE:260 FALSE:40
## Median :-2.303 Mode :character TRUE :40 TRUE :260
## Mean :-2.303
## 3rd Qu.:-2.303
## Max. :-2.303
## chain_step_id
## Min. : 1.00
## 1st Qu.: 75.75
## Median :150.50
## Mean :150.34
## 3rd Qu.:225.25
## Max. :300.00
## [1] 300 13
## dv_type dv_type_bin dv_varname id_from id_to beh_difference
## 1 Network 0 self$bipartite_rsienaDV 11 8 0
## 2 Network 0 self$bipartite_rsienaDV 4 6 0
## 3 Network 0 self$bipartite_rsienaDV 12 1 0
## 4 Network 0 self$bipartite_rsienaDV 9 1 0
## 5 Network 0 self$bipartite_rsienaDV 6 4 0
## 6 Network 0 self$bipartite_rsienaDV 3 10 0
## 7 Network 0 self$bipartite_rsienaDV 9 7 0
## 8 Network 0 self$bipartite_rsienaDV 9 6 0
## 9 Network 0 self$bipartite_rsienaDV 1 2 0
## 10 Network 0 self$bipartite_rsienaDV 5 4 0
## 11 Network 0 self$bipartite_rsienaDV 2 8 0
## 12 Network 0 self$bipartite_rsienaDV 4 4 0
## 13 Network 0 self$bipartite_rsienaDV 8 10 0
## 14 Network 0 self$bipartite_rsienaDV 4 3 0
## 15 Network 0 self$bipartite_rsienaDV 6 8 0
## 16 Network 0 self$bipartite_rsienaDV 6 3 0
## 17 Network 0 self$bipartite_rsienaDV 4 10 0
## 18 Network 0 self$bipartite_rsienaDV 12 3 0
## 19 Network 0 self$bipartite_rsienaDV 12 7 0
## 20 Network 0 self$bipartite_rsienaDV 5 4 0
## 21 Network 0 self$bipartite_rsienaDV 8 10 0
## 22 Network 0 self$bipartite_rsienaDV 7 2 0
## 23 Network 0 self$bipartite_rsienaDV 1 2 0
## 24 Network 0 self$bipartite_rsienaDV 10 6 0
## 25 Network 0 self$bipartite_rsienaDV 1 10 0
## 26 Network 0 self$bipartite_rsienaDV 1 7 0
## 27 Network 0 self$bipartite_rsienaDV 7 8 0
## 28 Network 0 self$bipartite_rsienaDV 2 4 0
## 29 Network 0 self$bipartite_rsienaDV 8 10 0
## 30 Network 0 self$bipartite_rsienaDV 7 10 0
## 31 Network 0 self$bipartite_rsienaDV 11 3 0
## 32 Network 0 self$bipartite_rsienaDV 10 4 0
## 33 Network 0 self$bipartite_rsienaDV 11 10 0
## 34 Network 0 self$bipartite_rsienaDV 1 4 0
## 35 Network 0 self$bipartite_rsienaDV 7 1 0
## 36 Network 0 self$bipartite_rsienaDV 9 9 0
## 37 Network 0 self$bipartite_rsienaDV 10 1 0
## 38 Network 0 self$bipartite_rsienaDV 10 7 0
## 39 Network 0 self$bipartite_rsienaDV 3 1 0
## 40 Network 0 self$bipartite_rsienaDV 7 7 0
## 41 Network 0 self$bipartite_rsienaDV 12 10 0
## 42 Network 0 self$bipartite_rsienaDV 7 8 0
## 43 Network 0 self$bipartite_rsienaDV 3 8 0
## 44 Network 0 self$bipartite_rsienaDV 6 9 0
## 45 Network 0 self$bipartite_rsienaDV 9 6 0
## 46 Network 0 self$bipartite_rsienaDV 6 10 0
## 47 Network 0 self$bipartite_rsienaDV 11 4 0
## 48 Network 0 self$bipartite_rsienaDV 5 8 0
## 49 Network 0 self$bipartite_rsienaDV 12 8 0
## 50 Network 0 self$bipartite_rsienaDV 3 5 0
## 51 Network 0 self$bipartite_rsienaDV 4 10 0
## 52 Network 0 self$bipartite_rsienaDV 5 10 0
## 53 Network 0 self$bipartite_rsienaDV 2 8 0
## 54 Network 0 self$bipartite_rsienaDV 12 1 0
## 55 Network 0 self$bipartite_rsienaDV 10 3 0
## 56 Network 0 self$bipartite_rsienaDV 2 6 0
## 57 Network 0 self$bipartite_rsienaDV 10 1 0
## 58 Network 0 self$bipartite_rsienaDV 7 7 0
## 59 Network 0 self$bipartite_rsienaDV 9 4 0
## 60 Network 0 self$bipartite_rsienaDV 12 5 0
## 61 Network 0 self$bipartite_rsienaDV 7 7 0
## 62 Network 0 self$bipartite_rsienaDV 2 2 0
## 63 Network 0 self$bipartite_rsienaDV 7 9 0
## 64 Network 0 self$bipartite_rsienaDV 11 5 0
## 65 Network 0 self$bipartite_rsienaDV 7 5 0
## 66 Network 0 self$bipartite_rsienaDV 9 6 0
## 67 Network 0 self$bipartite_rsienaDV 12 10 0
## 68 Network 0 self$bipartite_rsienaDV 9 10 0
## 69 Network 0 self$bipartite_rsienaDV 5 8 0
## 70 Network 0 self$bipartite_rsienaDV 11 9 0
## 71 Network 0 self$bipartite_rsienaDV 3 2 0
## 72 Network 0 self$bipartite_rsienaDV 10 10 0
## 73 Network 0 self$bipartite_rsienaDV 1 2 0
## 74 Network 0 self$bipartite_rsienaDV 12 7 0
## 75 Network 0 self$bipartite_rsienaDV 4 2 0
## 76 Network 0 self$bipartite_rsienaDV 5 9 0
## 77 Network 0 self$bipartite_rsienaDV 10 1 0
## 78 Network 0 self$bipartite_rsienaDV 4 5 0
## 79 Network 0 self$bipartite_rsienaDV 10 6 0
## 80 Network 0 self$bipartite_rsienaDV 9 9 0
## 81 Network 0 self$bipartite_rsienaDV 10 3 0
## 82 Network 0 self$bipartite_rsienaDV 1 1 0
## 83 Network 0 self$bipartite_rsienaDV 9 7 0
## 84 Network 0 self$bipartite_rsienaDV 9 3 0
## 85 Network 0 self$bipartite_rsienaDV 5 5 0
## 86 Network 0 self$bipartite_rsienaDV 11 6 0
## 87 Network 0 self$bipartite_rsienaDV 4 9 0
## 88 Network 0 self$bipartite_rsienaDV 9 7 0
## 89 Network 0 self$bipartite_rsienaDV 5 10 0
## 90 Network 0 self$bipartite_rsienaDV 1 2 0
## 91 Network 0 self$bipartite_rsienaDV 9 7 0
## 92 Network 0 self$bipartite_rsienaDV 9 5 0
## 93 Network 0 self$bipartite_rsienaDV 11 9 0
## 94 Network 0 self$bipartite_rsienaDV 8 8 0
## 95 Network 0 self$bipartite_rsienaDV 9 7 0
## 96 Network 0 self$bipartite_rsienaDV 8 6 0
## 97 Network 0 self$bipartite_rsienaDV 10 2 0
## 98 Network 0 self$bipartite_rsienaDV 6 3 0
## 99 Network 0 self$bipartite_rsienaDV 9 9 0
## 100 Network 0 self$bipartite_rsienaDV 10 6 0
## 101 Network 0 self$bipartite_rsienaDV 10 10 0
## 102 Network 0 self$bipartite_rsienaDV 8 5 0
## 103 Network 0 self$bipartite_rsienaDV 6 8 0
## 104 Network 0 self$bipartite_rsienaDV 4 7 0
## 105 Network 0 self$bipartite_rsienaDV 11 7 0
## 106 Network 0 self$bipartite_rsienaDV 11 1 0
## 107 Network 0 self$bipartite_rsienaDV 11 9 0
## 108 Network 0 self$bipartite_rsienaDV 4 2 0
## 109 Network 0 self$bipartite_rsienaDV 5 6 0
## 110 Network 0 self$bipartite_rsienaDV 1 5 0
## 111 Network 0 self$bipartite_rsienaDV 3 6 0
## 112 Network 0 self$bipartite_rsienaDV 11 3 0
## 113 Network 0 self$bipartite_rsienaDV 1 6 0
## 114 Network 0 self$bipartite_rsienaDV 1 1 0
## 115 Network 0 self$bipartite_rsienaDV 10 6 0
## 116 Network 0 self$bipartite_rsienaDV 6 2 0
## 117 Network 0 self$bipartite_rsienaDV 4 2 0
## 118 Network 0 self$bipartite_rsienaDV 5 9 0
## 119 Network 0 self$bipartite_rsienaDV 8 1 0
## 120 Network 0 self$bipartite_rsienaDV 4 1 0
## 121 Network 0 self$bipartite_rsienaDV 1 9 0
## 122 Network 0 self$bipartite_rsienaDV 2 9 0
## 123 Network 0 self$bipartite_rsienaDV 1 7 0
## 124 Network 0 self$bipartite_rsienaDV 1 10 0
## 125 Network 0 self$bipartite_rsienaDV 10 6 0
## 126 Network 0 self$bipartite_rsienaDV 12 3 0
## 127 Network 0 self$bipartite_rsienaDV 2 6 0
## 128 Network 0 self$bipartite_rsienaDV 7 4 0
## 129 Network 0 self$bipartite_rsienaDV 5 9 0
## 130 Network 0 self$bipartite_rsienaDV 4 5 0
## 131 Network 0 self$bipartite_rsienaDV 8 3 0
## 132 Network 0 self$bipartite_rsienaDV 9 8 0
## 133 Network 0 self$bipartite_rsienaDV 2 3 0
## 134 Network 0 self$bipartite_rsienaDV 4 6 0
## 135 Network 0 self$bipartite_rsienaDV 8 2 0
## 136 Network 0 self$bipartite_rsienaDV 10 9 0
## 137 Network 0 self$bipartite_rsienaDV 10 8 0
## 138 Network 0 self$bipartite_rsienaDV 11 10 0
## 139 Network 0 self$bipartite_rsienaDV 11 1 0
## 140 Network 0 self$bipartite_rsienaDV 5 8 0
## 141 Network 0 self$bipartite_rsienaDV 5 4 0
## 142 Network 0 self$bipartite_rsienaDV 4 7 0
## 143 Network 0 self$bipartite_rsienaDV 10 8 0
## 144 Network 0 self$bipartite_rsienaDV 11 6 0
## 145 Network 0 self$bipartite_rsienaDV 7 10 0
## 146 Network 0 self$bipartite_rsienaDV 6 4 0
## 147 Network 0 self$bipartite_rsienaDV 8 7 0
## 148 Network 0 self$bipartite_rsienaDV 2 10 0
## 149 Network 0 self$bipartite_rsienaDV 5 3 0
## 150 Network 0 self$bipartite_rsienaDV 3 6 0
## 151 Network 0 self$bipartite_rsienaDV 7 1 0
## 152 Network 0 self$bipartite_rsienaDV 8 8 0
## 153 Network 0 self$bipartite_rsienaDV 11 4 0
## 154 Network 0 self$bipartite_rsienaDV 3 1 0
## 155 Network 0 self$bipartite_rsienaDV 5 1 0
## 156 Network 0 self$bipartite_rsienaDV 7 7 0
## 157 Network 0 self$bipartite_rsienaDV 4 10 0
## 158 Network 0 self$bipartite_rsienaDV 9 3 0
## 159 Network 0 self$bipartite_rsienaDV 8 6 0
## 160 Network 0 self$bipartite_rsienaDV 11 3 0
## 161 Network 0 self$bipartite_rsienaDV 7 4 0
## 162 Network 0 self$bipartite_rsienaDV 3 4 0
## 163 Network 0 self$bipartite_rsienaDV 9 2 0
## 164 Network 0 self$bipartite_rsienaDV 7 2 0
## 165 Network 0 self$bipartite_rsienaDV 10 3 0
## 166 Network 0 self$bipartite_rsienaDV 3 10 0
## 167 Network 0 self$bipartite_rsienaDV 10 7 0
## 168 Network 0 self$bipartite_rsienaDV 7 8 0
## 169 Network 0 self$bipartite_rsienaDV 10 9 0
## 170 Network 0 self$bipartite_rsienaDV 7 2 0
## 171 Network 0 self$bipartite_rsienaDV 1 4 0
## 172 Network 0 self$bipartite_rsienaDV 12 3 0
## 173 Network 0 self$bipartite_rsienaDV 11 5 0
## 174 Network 0 self$bipartite_rsienaDV 12 6 0
## 175 Network 0 self$bipartite_rsienaDV 4 1 0
## 176 Network 0 self$bipartite_rsienaDV 6 10 0
## 177 Network 0 self$bipartite_rsienaDV 8 6 0
## 178 Network 0 self$bipartite_rsienaDV 4 9 0
## 179 Network 0 self$bipartite_rsienaDV 9 10 0
## 180 Network 0 self$bipartite_rsienaDV 4 7 0
## 181 Network 0 self$bipartite_rsienaDV 8 10 0
## 182 Network 0 self$bipartite_rsienaDV 2 10 0
## 183 Network 0 self$bipartite_rsienaDV 9 10 0
## 184 Network 0 self$bipartite_rsienaDV 6 9 0
## 185 Network 0 self$bipartite_rsienaDV 8 6 0
## 186 Network 0 self$bipartite_rsienaDV 1 1 0
## 187 Network 0 self$bipartite_rsienaDV 4 7 0
## 188 Network 0 self$bipartite_rsienaDV 12 2 0
## 189 Network 0 self$bipartite_rsienaDV 11 9 0
## 190 Network 0 self$bipartite_rsienaDV 9 9 0
## 191 Network 0 self$bipartite_rsienaDV 11 2 0
## 192 Network 0 self$bipartite_rsienaDV 1 8 0
## 193 Network 0 self$bipartite_rsienaDV 6 7 0
## 194 Network 0 self$bipartite_rsienaDV 6 3 0
## 195 Network 0 self$bipartite_rsienaDV 9 2 0
## 196 Network 0 self$bipartite_rsienaDV 11 4 0
## 197 Network 0 self$bipartite_rsienaDV 12 7 0
## 198 Network 0 self$bipartite_rsienaDV 7 5 0
## 199 Network 0 self$bipartite_rsienaDV 11 2 0
## 200 Network 0 self$bipartite_rsienaDV 5 1 0
## 201 Network 0 self$bipartite_rsienaDV 2 7 0
## 202 Network 0 self$bipartite_rsienaDV 6 1 0
## 203 Network 0 self$bipartite_rsienaDV 7 8 0
## 204 Network 0 self$bipartite_rsienaDV 8 9 0
## 205 Network 0 self$bipartite_rsienaDV 2 4 0
## 206 Network 0 self$bipartite_rsienaDV 12 10 0
## 207 Network 0 self$bipartite_rsienaDV 1 8 0
## 208 Network 0 self$bipartite_rsienaDV 9 10 0
## 209 Network 0 self$bipartite_rsienaDV 6 1 0
## 210 Network 0 self$bipartite_rsienaDV 10 8 0
## 211 Network 0 self$bipartite_rsienaDV 6 6 0
## 212 Network 0 self$bipartite_rsienaDV 12 3 0
## 213 Network 0 self$bipartite_rsienaDV 9 2 0
## 214 Network 0 self$bipartite_rsienaDV 4 10 0
## 215 Network 0 self$bipartite_rsienaDV 2 1 0
## 216 Network 0 self$bipartite_rsienaDV 10 8 0
## 217 Network 0 self$bipartite_rsienaDV 9 7 0
## 218 Network 0 self$bipartite_rsienaDV 11 4 0
## 219 Network 0 self$bipartite_rsienaDV 8 4 0
## 220 Network 0 self$bipartite_rsienaDV 3 8 0
## 221 Network 0 self$bipartite_rsienaDV 6 5 0
## 222 Network 0 self$bipartite_rsienaDV 5 8 0
## 223 Network 0 self$bipartite_rsienaDV 8 2 0
## 224 Network 0 self$bipartite_rsienaDV 2 3 0
## 225 Network 0 self$bipartite_rsienaDV 10 5 0
## 226 Network 0 self$bipartite_rsienaDV 10 2 0
## 227 Network 0 self$bipartite_rsienaDV 2 7 0
## 228 Network 0 self$bipartite_rsienaDV 2 6 0
## 229 Network 0 self$bipartite_rsienaDV 3 1 0
## 230 Network 0 self$bipartite_rsienaDV 3 5 0
## 231 Network 0 self$bipartite_rsienaDV 9 9 0
## 232 Network 0 self$bipartite_rsienaDV 9 3 0
## 233 Network 0 self$bipartite_rsienaDV 7 3 0
## 234 Network 0 self$bipartite_rsienaDV 7 2 0
## 235 Network 0 self$bipartite_rsienaDV 12 10 0
## 236 Network 0 self$bipartite_rsienaDV 3 4 0
## 237 Network 0 self$bipartite_rsienaDV 8 3 0
## 238 Network 0 self$bipartite_rsienaDV 6 9 0
## 239 Network 0 self$bipartite_rsienaDV 8 2 0
## 240 Network 0 self$bipartite_rsienaDV 8 10 0
## 241 Network 0 self$bipartite_rsienaDV 3 10 0
## 242 Network 0 self$bipartite_rsienaDV 10 5 0
## 243 Network 0 self$bipartite_rsienaDV 1 10 0
## 244 Network 0 self$bipartite_rsienaDV 8 4 0
## 245 Network 0 self$bipartite_rsienaDV 5 6 0
## 246 Network 0 self$bipartite_rsienaDV 11 4 0
## 247 Network 0 self$bipartite_rsienaDV 2 2 0
## 248 Network 0 self$bipartite_rsienaDV 1 2 0
## 249 Network 0 self$bipartite_rsienaDV 4 2 0
## 250 Network 0 self$bipartite_rsienaDV 10 2 0
## 251 Network 0 self$bipartite_rsienaDV 1 4 0
## 252 Network 0 self$bipartite_rsienaDV 11 2 0
## 253 Network 0 self$bipartite_rsienaDV 9 5 0
## 254 Network 0 self$bipartite_rsienaDV 3 4 0
## 255 Network 0 self$bipartite_rsienaDV 5 1 0
## 256 Network 0 self$bipartite_rsienaDV 5 6 0
## 257 Network 0 self$bipartite_rsienaDV 4 2 0
## 258 Network 0 self$bipartite_rsienaDV 6 9 0
## 259 Network 0 self$bipartite_rsienaDV 8 2 0
## 260 Network 0 self$bipartite_rsienaDV 1 4 0
## 261 Network 0 self$bipartite_rsienaDV 8 5 0
## 262 Network 0 self$bipartite_rsienaDV 1 10 0
## 263 Network 0 self$bipartite_rsienaDV 7 8 0
## 264 Network 0 self$bipartite_rsienaDV 4 4 0
## 265 Network 0 self$bipartite_rsienaDV 3 4 0
## 266 Network 0 self$bipartite_rsienaDV 5 5 0
## 267 Network 0 self$bipartite_rsienaDV 6 7 0
## 268 Network 0 self$bipartite_rsienaDV 5 1 0
## 269 Network 0 self$bipartite_rsienaDV 8 10 0
## 270 Network 0 self$bipartite_rsienaDV 12 6 0
## 271 Network 0 self$bipartite_rsienaDV 7 5 0
## 272 Network 0 self$bipartite_rsienaDV 11 2 0
## 273 Network 0 self$bipartite_rsienaDV 5 4 0
## 274 Network 0 self$bipartite_rsienaDV 9 5 0
## 275 Network 0 self$bipartite_rsienaDV 8 3 0
## 276 Network 0 self$bipartite_rsienaDV 11 5 0
## 277 Network 0 self$bipartite_rsienaDV 1 9 0
## 278 Network 0 self$bipartite_rsienaDV 4 2 0
## 279 Network 0 self$bipartite_rsienaDV 12 10 0
## 280 Network 0 self$bipartite_rsienaDV 5 9 0
## 281 Network 0 self$bipartite_rsienaDV 1 8 0
## 282 Network 0 self$bipartite_rsienaDV 10 1 0
## 283 Network 0 self$bipartite_rsienaDV 2 3 0
## 284 Network 0 self$bipartite_rsienaDV 11 5 0
## 285 Network 0 self$bipartite_rsienaDV 8 6 0
## 286 Network 0 self$bipartite_rsienaDV 9 9 0
## 287 Network 0 self$bipartite_rsienaDV 6 1 0
## 288 Network 0 self$bipartite_rsienaDV 9 7 0
## 289 Network 0 self$bipartite_rsienaDV 10 8 0
## 290 Network 0 self$bipartite_rsienaDV 4 6 0
## 291 Network 0 self$bipartite_rsienaDV 8 10 0
## 292 Network 0 self$bipartite_rsienaDV 8 4 0
## 293 Network 0 self$bipartite_rsienaDV 5 6 0
## 294 Network 0 self$bipartite_rsienaDV 6 5 0
## 295 Network 0 self$bipartite_rsienaDV 1 4 0
## 296 Network 0 self$bipartite_rsienaDV 12 10 0
## 297 Network 0 self$bipartite_rsienaDV 1 4 0
## 298 Network 0 self$bipartite_rsienaDV 9 5 0
## 299 Network 0 self$bipartite_rsienaDV 10 5 0
## 300 Network 0 self$bipartite_rsienaDV 7 2 0
## reciprocal_rate LogOptionSetProb LogChoiceProb diagonal stability
## 1 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 2 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 3 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 4 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 5 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 6 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 7 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 8 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 9 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 10 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 11 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 12 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 13 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 14 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 15 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 16 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 17 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 18 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 19 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 20 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 21 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 22 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 23 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 24 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 25 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 26 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 27 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 28 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 29 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 30 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 31 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 32 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 33 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 34 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 35 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 36 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 37 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 38 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 39 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 40 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 41 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 42 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 43 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 44 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 45 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 46 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 47 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 48 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 49 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 50 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 51 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 52 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 53 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 54 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 55 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 56 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 57 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 58 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 59 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 60 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 61 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 62 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 63 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 64 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 65 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 66 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 67 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 68 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 69 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 70 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 71 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 72 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 73 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 74 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 75 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 76 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 77 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 78 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 79 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 80 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 81 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 82 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 83 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 84 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 85 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 86 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 87 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 88 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 89 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 90 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 91 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 92 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 93 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 94 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 95 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 96 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 97 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 98 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 99 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 100 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 101 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 102 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 103 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 104 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 105 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 106 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 107 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 108 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 109 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 110 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 111 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 112 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 113 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 114 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 115 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 116 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 117 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 118 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 119 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 120 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 121 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 122 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 123 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 124 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 125 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 126 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 127 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 128 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 129 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 130 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 131 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 132 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 133 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 134 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 135 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 136 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 137 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 138 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 139 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 140 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 141 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 142 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 143 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 144 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 145 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 146 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 147 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 148 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 149 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 150 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 151 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 152 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 153 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 154 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 155 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 156 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 157 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 158 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 159 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 160 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 161 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 162 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 163 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 164 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 165 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 166 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 167 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 168 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 169 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 170 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 171 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 172 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 173 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 174 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 175 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 176 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 177 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 178 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 179 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 180 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 181 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 182 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 183 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 184 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 185 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 186 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 187 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 188 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 189 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 190 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 191 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 192 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 193 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 194 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 195 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 196 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 197 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 198 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 199 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 200 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 201 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 202 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 203 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 204 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 205 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 206 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 207 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 208 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 209 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 210 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 211 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 212 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 213 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 214 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 215 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 216 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 217 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 218 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 219 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 220 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 221 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 222 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 223 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 224 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 225 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 226 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 227 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 228 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 229 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 230 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 231 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 232 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 233 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 234 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 235 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 236 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 237 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 238 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 239 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 240 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 241 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 242 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 243 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 244 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 245 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 246 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 247 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 248 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 249 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 250 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 251 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 252 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 253 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 254 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 255 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 256 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 257 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 258 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 259 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 260 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 261 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 262 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 263 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 264 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 265 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 266 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 267 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 268 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 269 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 270 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 271 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 272 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 273 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 274 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 275 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 276 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 277 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 278 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 279 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 280 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 281 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 282 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 283 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 284 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 285 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 286 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 287 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 288 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 289 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 290 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 291 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 292 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 293 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 294 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 295 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 296 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 297 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 298 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 299 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 300 0.08333333 -2.484907 -2.302585 FALSE FALSE
## tie_change chain_step_id
## 1 TRUE 1
## 2 TRUE 2
## 3 TRUE 3
## 4 TRUE 4
## 5 TRUE 5
## 6 FALSE 5
## 7 TRUE 7
## 8 TRUE 8
## 9 TRUE 9
## 10 TRUE 10
## 11 TRUE 11
## 12 TRUE 12
## 13 FALSE 12
## 14 TRUE 14
## 15 TRUE 15
## 16 TRUE 16
## 17 FALSE 16
## 18 TRUE 18
## 19 TRUE 19
## 20 TRUE 20
## 21 FALSE 20
## 22 TRUE 22
## 23 TRUE 23
## 24 TRUE 24
## 25 FALSE 24
## 26 TRUE 26
## 27 TRUE 27
## 28 TRUE 28
## 29 FALSE 28
## 30 FALSE 28
## 31 TRUE 31
## 32 TRUE 32
## 33 FALSE 32
## 34 TRUE 34
## 35 TRUE 35
## 36 TRUE 36
## 37 TRUE 37
## 38 TRUE 38
## 39 TRUE 39
## 40 TRUE 40
## 41 FALSE 40
## 42 TRUE 42
## 43 TRUE 43
## 44 TRUE 44
## 45 TRUE 45
## 46 FALSE 45
## 47 TRUE 47
## 48 TRUE 48
## 49 TRUE 49
## 50 TRUE 50
## 51 FALSE 50
## 52 FALSE 50
## 53 TRUE 53
## 54 TRUE 54
## 55 TRUE 55
## 56 TRUE 56
## 57 TRUE 57
## 58 TRUE 58
## 59 TRUE 59
## 60 TRUE 60
## 61 TRUE 61
## 62 TRUE 62
## 63 TRUE 63
## 64 TRUE 64
## 65 TRUE 65
## 66 TRUE 66
## 67 FALSE 66
## 68 FALSE 66
## 69 TRUE 69
## 70 TRUE 70
## 71 TRUE 71
## 72 FALSE 71
## 73 TRUE 73
## 74 TRUE 74
## 75 TRUE 75
## 76 TRUE 76
## 77 TRUE 77
## 78 TRUE 78
## 79 TRUE 79
## 80 TRUE 80
## 81 TRUE 81
## 82 TRUE 82
## 83 TRUE 83
## 84 TRUE 84
## 85 TRUE 85
## 86 TRUE 86
## 87 TRUE 87
## 88 TRUE 88
## 89 FALSE 88
## 90 TRUE 90
## 91 TRUE 91
## 92 TRUE 92
## 93 TRUE 93
## 94 TRUE 94
## 95 TRUE 95
## 96 TRUE 96
## 97 TRUE 97
## 98 TRUE 98
## 99 TRUE 99
## 100 TRUE 100
## 101 FALSE 100
## 102 TRUE 102
## 103 TRUE 103
## 104 TRUE 104
## 105 TRUE 105
## 106 TRUE 106
## 107 TRUE 107
## 108 TRUE 108
## 109 TRUE 109
## 110 TRUE 110
## 111 TRUE 111
## 112 TRUE 112
## 113 TRUE 113
## 114 TRUE 114
## 115 TRUE 115
## 116 TRUE 116
## 117 TRUE 117
## 118 TRUE 118
## 119 TRUE 119
## 120 TRUE 120
## 121 TRUE 121
## 122 TRUE 122
## 123 TRUE 123
## 124 FALSE 123
## 125 TRUE 125
## 126 TRUE 126
## 127 TRUE 127
## 128 TRUE 128
## 129 TRUE 129
## 130 TRUE 130
## 131 TRUE 131
## 132 TRUE 132
## 133 TRUE 133
## 134 TRUE 134
## 135 TRUE 135
## 136 TRUE 136
## 137 TRUE 137
## 138 FALSE 137
## 139 TRUE 139
## 140 TRUE 140
## 141 TRUE 141
## 142 TRUE 142
## 143 TRUE 143
## 144 TRUE 144
## 145 FALSE 144
## 146 TRUE 146
## 147 TRUE 147
## 148 FALSE 147
## 149 TRUE 149
## 150 TRUE 150
## 151 TRUE 151
## 152 TRUE 152
## 153 TRUE 153
## 154 TRUE 154
## 155 TRUE 155
## 156 TRUE 156
## 157 FALSE 156
## 158 TRUE 158
## 159 TRUE 159
## 160 TRUE 160
## 161 TRUE 161
## 162 TRUE 162
## 163 TRUE 163
## 164 TRUE 164
## 165 TRUE 165
## 166 FALSE 165
## 167 TRUE 167
## 168 TRUE 168
## 169 TRUE 169
## 170 TRUE 170
## 171 TRUE 171
## 172 TRUE 172
## 173 TRUE 173
## 174 TRUE 174
## 175 TRUE 175
## 176 FALSE 175
## 177 TRUE 177
## 178 TRUE 178
## 179 FALSE 178
## 180 TRUE 180
## 181 FALSE 180
## 182 FALSE 180
## 183 FALSE 180
## 184 TRUE 184
## 185 TRUE 185
## 186 TRUE 186
## 187 TRUE 187
## 188 TRUE 188
## 189 TRUE 189
## 190 TRUE 190
## 191 TRUE 191
## 192 TRUE 192
## 193 TRUE 193
## 194 TRUE 194
## 195 TRUE 195
## 196 TRUE 196
## 197 TRUE 197
## 198 TRUE 198
## 199 TRUE 199
## 200 TRUE 200
## 201 TRUE 201
## 202 TRUE 202
## 203 TRUE 203
## 204 TRUE 204
## 205 TRUE 205
## 206 FALSE 205
## 207 TRUE 207
## 208 FALSE 207
## 209 TRUE 209
## 210 TRUE 210
## 211 TRUE 211
## 212 TRUE 212
## 213 TRUE 213
## 214 FALSE 213
## 215 TRUE 215
## 216 TRUE 216
## 217 TRUE 217
## 218 TRUE 218
## 219 TRUE 219
## 220 TRUE 220
## 221 TRUE 221
## 222 TRUE 222
## 223 TRUE 223
## 224 TRUE 224
## 225 TRUE 225
## 226 TRUE 226
## 227 TRUE 227
## 228 TRUE 228
## 229 TRUE 229
## 230 TRUE 230
## 231 TRUE 231
## 232 TRUE 232
## 233 TRUE 233
## 234 TRUE 234
## 235 FALSE 234
## 236 TRUE 236
## 237 TRUE 237
## 238 TRUE 238
## 239 TRUE 239
## 240 FALSE 239
## 241 FALSE 239
## 242 TRUE 242
## 243 FALSE 242
## 244 TRUE 244
## 245 TRUE 245
## 246 TRUE 246
## 247 TRUE 247
## 248 TRUE 248
## 249 TRUE 249
## 250 TRUE 250
## 251 TRUE 251
## 252 TRUE 252
## 253 TRUE 253
## 254 TRUE 254
## 255 TRUE 255
## 256 TRUE 256
## 257 TRUE 257
## 258 TRUE 258
## 259 TRUE 259
## 260 TRUE 260
## 261 TRUE 261
## 262 FALSE 261
## 263 TRUE 263
## 264 TRUE 264
## 265 TRUE 265
## 266 TRUE 266
## 267 TRUE 267
## 268 TRUE 268
## 269 FALSE 268
## 270 TRUE 270
## 271 TRUE 271
## 272 TRUE 272
## 273 TRUE 273
## 274 TRUE 274
## 275 TRUE 275
## 276 TRUE 276
## 277 TRUE 277
## 278 TRUE 278
## 279 FALSE 278
## 280 TRUE 280
## 281 TRUE 281
## 282 TRUE 282
## 283 TRUE 283
## 284 TRUE 284
## 285 TRUE 285
## 286 TRUE 286
## 287 TRUE 287
## 288 TRUE 288
## 289 TRUE 289
## 290 TRUE 290
## 291 FALSE 290
## 292 TRUE 292
## 293 TRUE 293
## 294 TRUE 294
## 295 TRUE 295
## 296 FALSE 295
## 297 TRUE 297
## 298 TRUE 298
## 299 TRUE 299
## 300 TRUE 300
##
## 33.33%
## 66.67%
## 100.00%
## ===================================================================================================
## Model 1
## ---------------------------------------------------------------------------------------------------
## Rate parameter period 1 0.0792 (0.0787)
## outdegree (density) 0.0000 (0.0000)
## indegree - popularity 0.0000 (0.0000)
## outdegree - activity 0.0000 (0.0000)
## XW=>X closure of self$component_1_coDyadCovar 1.0000 (0.0000) ***
## self$component_2_coDyadCovar 0.0000 (0.0000)
## self$strat_1_coCovar ego 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar 0.0000 (0.0000)
## self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.0000 (0.0000)
## ---------------------------------------------------------------------------------------------------
## Iterations 300
## ===================================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
## 1st and last state of the bipartite matrix system
print(env1$bipartite_matrix_init )
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 0 0 0 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0
## [6,] 0 0 0 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 0 0 0
## [8,] 0 0 0 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0 0 0 0
## [11,] 0 0 0 0 0 0 0 0 0
## [12,] 0 0 0 0 0 0 0 0 0
print(env1$bi_env_arr[,, dim(env1$bi_env_arr)[3] ] )
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 0 0 1 1 0 1 0
## [2,] 1 0 1 0 0 1 0 0 1
## [3,] 1 1 0 0 0 0 0 0 0
## [4,] 0 0 1 0 0 1 0 0 0
## [5,] 0 0 1 0 0 0 0 0 0
## [6,] 1 1 1 0 0 1 0 0 0
## [7,] 0 1 1 0 1 0 0 1 1
## [8,] 1 0 1 1 0 1 1 0 1
## [9,] 1 1 1 1 0 1 1 1 0
## [10,] 0 1 1 1 1 1 0 1 0
## [11,] 0 0 1 1 0 0 1 1 0
## [12,] 0 1 0 0 1 0 1 1 0
Snapshopts of the the biparite network and the social and component epistasis interactions are taken at fixed step intervals to show the evolving multidimensional coupled search environment (actors and components)
# snapshot_ids <- round( seq(1, dim(env1$bi_env_arr)[3], length=3 ) )
# snapshot_ids <- c(1, 1+ (1:6)*env1$M)
snapshot_ids <- c(1, (1:8)*10, dim(env1$bi_env_arr)[3] )
for (i in 1:length(snapshot_ids)) {
step <- snapshot_ids[ i ]
mat <- env1$bi_env_arr[,,step]
env1$plot_bipartite_system_from_mat(mat, step)
}
##
env1$plot_component_degrees(loess_span = 0.35)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## [1] 3600 5
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## [1] 3600 5
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Time series is simulated decision steps.
##
env1$plot_utility_components(loess_span=0.25)
## `geom_smooth()` using formula = 'y ~ x'
env1$plot_actor_utility_strategy_summary(loess_span=0.5)